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Modelling Human Motion in a Dynamic Environment

 

 

This project focuses on a novel framework for modelling human-robot interactions. It is expected to result in significant improvements in the accuracy of models of human motion, with a minimal increase in computational burden over existing methods.

Specifically, the current work investigates the modelling of the behaviour of people within a dynamic environment. It is well known that changing aspects of the environment will influence the way people move within it. The correlations between the behaviour of people and the state of the environment have not, however, been investigated deeply. The project adopts an interest-based approach to investigate both the gross motion of people through a space and the effects which controllable aspects of the environment will have on this motion.

The primary modelling technique being applied is Dynamic Bayesian Networks (DBNs), a probabilistic modelling technique with scope for great flexibility. DBNs require training, and the application of intelligent training techniques to these systems allows the learning of human behaviours from existing data. The Fish-Bird project is being used as a data-collection tool to facilitate this work, and over 7 Gb of log files have been collected of (anonymous) human motion through the installation space.

A diagram showing 2 consecutive slices of a DBN. The arrows show the probabilistic relationships beteween the observed (grey) and hidden (white) variables. These relationships occur both within a time slice, and also between two time-slices, allowing estimation of the hidden states and prediction of future states given the present and past states.

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